In general, self localization visual information is related tophotometry. The typical process of self localization involves two processes. The first process is to extract features from the image and the second process is to use these extracted information for localization. “J. Yuan. A general photogrammetric method for determining object position and orientation. pages 129-142, 1989” presents a general method for determining the three-dimensional position and orientation of an object relative to a camera based on a two-dimensional image of known feature points located on the object. “O. Leboulleux R Horaud, B. Conio and B. Lacolle. An analytic solution for the perspective 4-point problem. pages 33-44, 1989” analytically deals with the perspective n-point (PnP) problems with four correspondence of scene objects. The self localization is developed with a focus of applying the algorithm for robot navigation.
A simple method for visual localization which allows a robot to determine its absolute position with a view of single landmark in one image is presented in “http://www.tamron.com/.” In this algorithm, the actual camera plane is perpendicular to the optical axis and aligned with the optical axis at a distance f called focal length.
To track the landmark model, Lucas-Kanade optical flow algorithm is applied by using gradient descent. This algorithm has feasible real-time performance in indoor environments. However, the approach has the limitation in that with the pinhole camera model only one correspondence can be established.
Another localization algorithm which is based on comparing the images taken in advance and taken during navigation is discussed in “J. Borenstein, H. Everett, L. Feng, and D. Wehe, “Mobile robot positioning: Sensors and techniques,” Journal of Robotic Systems, vol. 14, no. 4, pp. 231-249, 1997”. In this scheme, the shape and the coordinate of images are stored in memory efficient format for quick retrieval and comparison. This algorithm has restriction on the shape of landmark and is not suitable in open area.
Similar method is presented where planar landmarks are used in visual localization of a mobile robot in indoor environment, “F. Lerasle V. Ayala, J. B. Hayet and M. Devy. Visual localization of a mobile robot in indoor environment using planar landmarks. In Proceeding of the 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 275-280, 2000”. Scale Invariant Feature Transform (SIFT) developed for image feature generation in object recognition application is used for robot localization in “S. Se, D. Lowe, and J. Little. Vision-based mobile robot localization and mapping using scale-invariant features. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 2051-2058, Seoul, Korea, May 2001”.
The invariant characteristic of SIFT are captured by three images and stereo-matched to elect landmark that later used to compute 3-D world coordinate relative to the robot. This algorithm use three cameras requires expensive computational power.